A topology-constrained sparse convolutional network (SpTopoNet), which models the topological structure of moving object trajectories in event point clouds and introduces an event topology-aware loss function that leverages topological correlations to guide the network to maintain structural integrity of target event patterns.
Remote sensing object detection is a fundamental task in ground scene observation and analysis. Despite the currently discrete-frame detectors achieves remarkable performance, they still suffer from three critical limitations: 1) mainstream architectures regress spatial locations independently, making it difficult to e...
Shi-Long Jing, Heng-Yi Lv, Yu-Chen Zhao et al.· IEEE Geoscience and Remote S...· 0 citations
This work proposes FLEET (Feature Learning from Events via Efficient Tokenization), a feature extractor that processes event sequences directly and decouples inference cost of the feature extractor's backbone from the sensor's resolution, enabling end-to-end learning without auxiliary losses.
T. Gottwald, Maximilian Schier, Melanie Schaller et al.· 0 citations
This work establishes a new performance benchmark for classical object discovery in event data, providing a highly scalable, training-free solution for resource-constrained visual perception.
P. G. Shenwai, H. Singh, Sridhar Ravi· 0 citations
This work investigates how temporal information can be encoded directly within the event representation, proposing a confidence-normalized continuous multi-timescale representation based on logarithmic B-spline temporal encoding together with a geometry-aware local confidence mechanism that exploits the spatial structu...
Fredrik Lundell, Per-Erik Forssén, Mårten Wadenbäck et al.· 1 citation
HSAR-DETR is proposed, a detection framework that jointly improves hierarchical feature representation, cross-scale refinement, and geometry-aware localization and experimental results on the VisDrone, RSOD, and TinyPerson datasets demonstrate improved detection performance.
A novel Structured Topology of Gridpoints (STG) framework, operating strictly under standard full-box annotations without any extra visibility supervision, aims to achieve implicit, fine-grained local semantic compensation in pedestrian detection in crowds.
Tian Qiu, Jifeng Shen, Xin Zuo· Italian National Conference...· 0 citations
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